Desistance from physical abuse in a national study of Nepal: Protective informal social control and self-compassion
Bibliographic record
Abstract
BACKGROUND: Research on the conditions under which perpetrators desist from child maltreatment has seen greater attention as part of the efforts to break the cycle of maltreatment. New theoretical insights suggest that informal actions (herein protective informal social control of child maltreatment) by network members which communicate warmth, empathy with victim distress, and promote the modeling of positive parenting practices are more likely to increase maltreatment desistance. Likewise, parents' desistance from maltreatment is theorized to impact on adolescents' (victim) cognition and self-compassion. OBJECTIVE: This study examined the relationship among protective informal social control of child maltreatment (protective ISC_CM) by social networks, physical abuse desistance, and adolescent self-compassion. PARTICIPANTS AND SETTING: A nationally representative sample of 1100 mothers and their adolescent children (aged 11-15) in Nepal was obtained. METHODS: Questionnaires were administered to mothers and their adolescent children independently. Hypotheses were tested using regression models with standard errors corrected for clustering within wards. RESULTS: More than 1 in 7 mothers reported perpetrating physical abuse in the past year, and 1 in every 5 adolescents reported being victims of physical abuse. Odds of abuse desistance increase by roughly 10 % for each act of protective ISC_CM reported by the mother. Also, odds of abuse desistance associated with higher adolescent self-compassion, and acts of protective ISC_CM associated with higher levels of adolescent self-compassion. CONCLUSION: The findings suggest that interventions to boost desistance from maltreatment and break the cycle of abuse in Nepal, should focus on promoting protective informal social control actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".